Criminal incident prediction using a point-pattern-based density model

@article{Liu2003CriminalIP,
  title={Criminal incident prediction using a point-pattern-based density model},
  author={Hua Liu and Donald E. Brown},
  journal={International Journal of Forecasting},
  year={2003},
  volume={19},
  pages={603-622}
}

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References

SHOWING 1-10 OF 44 REFERENCES

A methodological model

This model goes beyond simple cluster or centroid analysis by employing specific serial murder research, overlapping modified Pareto functions, and Manhattan distances to generate a choropleth probability map that indicates the areas most likely to be associated to the offender.

The Regional Crime Analysis Program (ReCAP): a framework for mining data to catch criminals

  • Donald E. Brown
  • Computer Science
    SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218)
  • 1998
ReCAP (Regional Crime Analysis Program) is described, which was built to provide crime analysts with both data fusion and data mining technologies.

Urban Structure and Criminal Mobility

Llrban structure may be viewed as an abstract or generalized description of the distribution of phenomena in urban geographic space. The concept incorporates such ideas as pattern, distance,

STAC HOT SPOT AREAS: A STATISTICAL TOOL FOR LAW ENFORCEMENT DECISIONS 1

The last ten or fifteen years have seen a quiet revolution in criminology and criminal justice. There has been a vast improvement in the quality and quantity of data, and in the availability of that

Patterns of Crime in a University Housing Project

In the past fifteen years it has been suggested by numerous authors that spatial analysis of urban areas can be employed by urban planners, architects, and others in related fields in order to

An examination of procedures for determining the number of clusters in a data set

A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters. To

Mixture models : inference and applications to clustering

The Mixture Likelihood Approach to Clustering and the Case Study Homogeneity of Mixing Proportions Assessing the Performance of the Mixture likelihood approach toClustering.

Statistics for Spatial Data.

Statistics for Spatial Data GEOSTATISTICAL DATA Geostatistics Spatial Prediction and Kriging Applications of Geostatistics Special Topics in Statistics for Spatial Data LATTICE DATA Spatial Models on

An examination of procedures for determining the number of clusters in a data set

The aim of this paper is to compare three methods based on the hypervolume criterion with four other well-known methods for determining the number of clusters on artificial data sets.